LLM-Adapters
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
Integrate and fine-tune large language models efficiently using a framework that supports various adapter methods for different tasks.
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Free · no card · unsubscribe anytimeCode for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
LLM-Adapters has 1.2k stars on GitHub. It has been forked 115 times. LLM-Adapters is written mainly in Python. It has been in active development since 2023. LLM-Adapters is available under the Apache-2.0 license. Its main topics are adapters, fine-tuning, large-language-models, parameter-efficient.
What is LLM-Adapters?
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
Is LLM-Adapters open source?
LLM-Adapters is an open-source project. It is released under the Apache-2.0 license.
Is LLM-Adapters free?
Yes. LLM-Adapters is free and open source — you can use, modify and self-host it.
What license does LLM-Adapters use?
LLM-Adapters is available under the Apache-2.0 license.
What language is LLM-Adapters written in?
LLM-Adapters is written mainly in Python.
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